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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Pandey, Pragati Shrivastava, Ankit Chandra Singh, Ajay Shanker Sharma, Nisha Galgotias University School of Computing Science & Engineering India

ISBN: (纸本)9798350356816

The rapid proliferation of unwired techniques and mobile instruments had profound effect on daily basis. To leverage the potential of these technologies in the hospitality industry, there have been early attempts to merge them. This research project focuses on automating the pizza ordering process in restaurants with the goal of enhancing the overall eating occurrence for consumers. This paper delves into model and execution of an Automated Pizza Ordering System for restaurant *** system leverages unwired data connectivity for communication with servers. A dedicated Android application on the user's mobile device provides access to the eating place menu. Consumers can place their orders with their mobile devices, and these orders detail are transmitted wirelessly to a central database, which then routed to the cooking area and cash handler. This technology enables eating place owners to easily manage menu updates and *** use of wireless applications on mobile devices offers convenience, streamlines operations, reduces the potential for human mistakes, and provides on-time customer feedback. This system effectively addresses limitations of previous PDA-based pizza ordering system and proves to be a cost-effective and efficient alternative to traditional multi-touch eating place management systems. © 2024 IEEE.

关键词： Information management

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Emotion detection: Facial emotion detection using ML 4th

Emotion detection: Facial emotion detection using ML

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Sharad Chauhan, Ritik Vajpayee, Pooja School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Cognitive perception of images is an intense task, like guessing the truth of a thought or a mystery. In this process, we use different methods to solve the need to know the job. In recent years, emotional intelligence has received widespread attention due to its important role in improving human-computer interaction and emotional analysis. Because of its processes for extracting visual features, deep neural networks-particularly convolutional neural network (CNN)-are frequently employed in FER. A lot of research has been done on CNN, but only a few areas have solved the facial recognition (FER) problem. After many reports and studies, the shortcoming of most existing models is that they often consider the front profile and ignore the side profile of the current image, although different angular views are also important for the perspective model. This helps you make informed decisions about personal needs, providing support, or security threats. However, this proved to be too complex for computers, and feature extraction needed a wide range of image processing methods. Numerous machine learning techniques are appropriate for this particular purpose. To use machine learning for any kind of detection, an algorithm must first be trained and then tested on relevant data. This article examines some machine learning algorithms and strategies for extraction that aid in the analysis of human emotion. This review contains a comprehensive review of a variety of materials, methods, and measures commonly used in psychology research. It also highlights the challenges and limitations of current models, including dealing with uncertainties and cultural differences. Face recognition in large datasets poses additional challenges due to the diversity of images as well as the differences between side and front views. © 2025 the Author(s).

关键词： Convolutional neural networks

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Design and Development of Defected Ground Structure Fractal Antenna based on Osgood Curve for C-band Modern Wireless Communications

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 23-29页

作者： Singh, Gurmeet Kumar, Ashish School of Computing Science and Engineering Galgotias University Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Multifunctional behavioral antennas are one of the significant features of the modern wireless communication systems. This is due to the fact that the miniaturized sizes of the devices demand compact and low-profile antennas that cover limited space and provide multi-frequency functionalities over a wide range of the spectrum. In view of these prerequisites, this paper presents the two design approaches utilizing Osgood Curve Fractal structure as the antenna element with different feeding techniques. The proposed antennas resonate at multiple frequencies covering S-and C-band with excellent impedance matching. The designs have been optimized in terms of scaling factor and position of microstrip feed line with peak gain of 4.5 dBi. Design 1 has significant bandwidth for required applications;however, design 2 possesses low bandwidth with small frequency ratio. Both the proposed designs are fabricated, and results are quite promising. The proposed designs can be utilized for various sub-6 GHz 5G automotive applications as it can easily integrated in the various parts of the modern passenger car. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Bandwidth

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House Price Prediction Using Machine Learning 1

House Price Prediction Using Machine Learning

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Mishra, Amandeep Jason Singh, Yogesh Rajakumar, P. Partheeban, N. Indervati Galgotias University School of Computing Science & Engineering UP India

ISBN: (纸本)9798350356816

Machine Learning (ML) has become increasingly prominent in various domains, including everyday conversations, product recommendations, and even the medical field. It has also played a vital role in enhancing customer services and ensuring safer transportation systems. Given ML's widespread impact, we aim to incorporate ML techniques into our project. The housing market, known for its competitiveness and constant fluctuations, poses challenges for individuals seeking to purchase a new home within their budget. The existing system lacks accurate predictions of future market trends, resulting in price increases. Therefore, our project's primary objective is to predict house prices with precision, eliminating any potential losses. To achieve this, we consider multiple factors that influence house prices and strive to provide customers with successful predictions that align with their budgets and preferences. Our proposed housing price prediction model employs AI algorithms such as Linear, Decision Tree, K-Means, and Random Forest Regression. The goal of this strategy is to eliminate the need for individuals to rely on intermediaries when making decisions on investments in real estate. The research findings highlight that Random Forest Regression offers the highest level of accuracy. © 2024 IEEE.

关键词： Houses

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The Design and Development of web3.0: The Internet's Future Architecture 1

The Design and Development of web3.0: The Internet's Future ...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Gautam Dut, Yagya Kumar, Rajneesh Rajakumar, P. Indervati Galgotias University School of Computing Science & Engineering UP India

ISBN: (纸本)9798350356816

The internet is regarded as the most vital international data media. It allows consumers to browse and write information about the world around them. The development of the web has been continuously increasing over the years. In the past couple of years, it has been able to develop various versions. In 1991, the concept of the exemplary net was confirmed. This means that web1.0 is tied to statistics and is used to gather information. The rise of net 2.0 allowed individuals to create and distribute content. Web 3.0 is regarded as the most significant update to the internet. The goal of the semantic internet is to create and think about new information in a way that is most appropriate for humans. The rise of artificial intelligence (AI) is expected to make people more interested in facts. The goal of the Metaverse is to consolidate various virtual spaces. Through the utilization of elevated reality, people can indulge in various diversions and undertakings. The rise of the Internet has created various new obstacles and opportunities for education and online learning. The previous generation of the Internet was known as Web 1.0, which was initially designed as a Read-Only medium. Web 3.0, which is currently in development, is a more technologically advanced version of the web that enables people to perform various tasks and read and write. The development of Web 3.0 has led to the creation of new tools and technologies that can help support online learning and education. This essay aims to provide a brief overview of the various aspects of Web 3.0. The goal of Web 3.0 is to provide a more personalized and comprehensive education and learning experience. It also supports knowledge building through the use of the Semantic Web. © 2024 IEEE.

关键词： Traffic congestion

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Advanced Congestion Management System 1

Advanced Congestion Management System

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Rajakumar, P. Baliyan, Akshay Chaudhary, Deepanshi Panwar, Sachin Kumari, Richa Galgotias University School of Computing Science & Engineering UP India

ISBN: (纸本)9798350356816

Congestion is poised to become a significant problem in numerous Asian cities and other countries. The causes of traffic congestion include signal malfunctions, inadequate law enforcement, and risky traffic management. The limited expansion possibilities of existing infrastructure in Indian cities present a major challenge, making effective traffic management the only viable option. The negative impact of congestion on the economy, environment, and overall quality of life necessitates prompt action. Various traffic management technologies are available, such as video investigation, infrared, inducible loop detection, and sensor networks, all of which contribute to efficient traffic management. Nevertheless, these systems are costly to install and maintain and take considerable time to implement. In this regard, a cost-effective alternative technology called radio frequency identification (RFID) is introduced, offering real-time traffic control capabilities comparable to the current signal system. Compared to other congestion management methods, this innovative technology requires less time and cost for deployment. By identifying bottlenecks early on, this groundbreaking technology enables proactive measures, saving drivers time and money while alleviating traffic congestion. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Deciphering Brain Tumor Diagnosis: A CNN-Based Deep Learning Approach 4

Deciphering Brain Tumor Diagnosis: A CNN-Based Deep Learning...

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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Tiwari, Pradeep Kumar Johri, Prashant Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350360165

A brain tumor is defined as the rapid growth of abnormal cells in the brain. Magnetic resonance imaging (MRI) is the best way to diagnose these tumors. Using MRI images, doctors can carefully identify and define specific areas of soft tissue growth to determine the presence of brain tumors. Today, brain cancer diagnosis uses machine learning and deep learning algorithms along with advances in artificial intelligence. Using these algorithms has many benefits, such as increasing the brain's focus, reducing errors, and providing more accurate results. These benefits increase the efficiency of the decision-making process and make it easier for the patient to choose the most appropriate treatment options. This article introduces the use of convolutional neural networks (CNN) to detect brain tumors and evaluate their consequences. The main goal is to use neural networks, especially deep learning techniques, to identify brain tumors. From the results obtained in the training and testing phase, the pre-trained model achieved an accuracy of 0.9664 and a loss of 0.0934. Obtained are indicators for the classification of glioma, meningioma, non-neoplastic and pituitary tumors. Precision, recall, and F1 scores for glioma were 0.9781, 0.9477, and 0.9627, respectively. In the absence of tumor, precision, recall, and F1 scores were 0.9790, 0.9943, and 0.9866, respectively. The precision, sensitivity and harmonic mean values of the pituitary gland are 0.9589, 0.9951 and 0.9767, respectively. After analyzing the dataset, it is clear that convolutional neural networks (CNN) are the most reliable method for predicting the presence of brain tumors.s © 2024 IEEE.

关键词： Convolutional neural networks

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Face Recognition-Based Automated Attendance System 5

Face Recognition-Based Automated Attendance System

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Tripathi, Arunendra Mani Rai, Ashish Kumar Pandey, Divyanshu Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

The attendance system is an essential part of any organization, and the traditional manual attendance system can be very prone to errors and very time-consuming. This paper introduces a sophisticated automated attendance system that combines Principal Component Analysis (PCA) algorithm and Artificial Intelligence (AI) for enhanced accuracy and efficiency. In contrast to error-prone and time-consuming manual attendance processes, our system leverages PCA for robust facial feature analysis, allowing precise identification of students or workers. The integration of AI further refines the recognition process, adapting and learning from diverse facial characteristics to improve system performance over time. © 2024 IEEE.

关键词： AI eigenfaces eigenvectors Facial Recognition FLTK OpenCV PCA

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Smart Health Diagnosis:Harnessing Data Mining for Predictive Healthcare 1

Smart Health Diagnosis:Harnessing Data Mining for Predictive...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Indrakumari, R. Pandey, Vishal Sinha, Ashish Kumar Kumar, Ankesh Galgotias University School of Computer Science and Engineering India Galgotias University School of Computing Application and Technology India

ISBN: (纸本)9798350356816

The use of Smart Health Diagnosis: Data Mining for Predictive Healthcare is to provide a user- friendly and easily accessible platform where individuals can get instant health advice through an intelligent online healthcare system. By the analysis of the reported symptoms, the system can predict potential future diseases and provide relevant recommendations to healthcare providers on courses of action. Intelligent methods accurately determine potential interferences based on patient symptoms. Medical record database was used and symptoms were assigned to underlying diseases using Naive Bayesian calculations. Fast, reliable access to health care is a cornerstone of individual and social health but many people face barriers in receiving timely care. We are trying to solve these problems by introducing Health Prediction System, a revolutionary online healthcare solution that uses data mining for quick support and customized recommendations. © 2024 IEEE.

关键词： Electronic health record

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Comprehensive Approach to Motion Planning for Self-Driving Vehicles: Trajectory Roll Out, Route Planning, and Safe Maneuver Execution 3rd

Comprehensive Approach to Motion Planning for Self-Driving V...

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Adhikari, Vishal Singh Arora, Gautam Koli, Vrushabh Namdeo Dagur, Arvind School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9789819725076

In this paper, a comprehensive strategy for motion planning in self-driving vehicles is presented. The suggested approach, called trajectory roll out motion planning, allows for the computation of time to collision with both stationary and constantly-moving obstacles, facilitating the creation of routes in intricate road networks. The method includes the definition of high-level vehicle behaviors and transitions, which enable the autonomous vehicle to navigate intersections, pass parked vehicles, and merge with other vehicles. Additionally, kinematically feasible paths are calculated in the presence of static barriers. Velocity profiles are also generated to fulfill speed, curvature, and motion planning constraints of moving objects. The efficacy of the approach is demonstrated by testing and debugging in the CARLA simulator, with successful execution of planned maneuvers in the real world. The research findings offer valuable insights and techniques for motion planning in autonomous vehicles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Trajectories

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